Meta 新论文让智能体学会管理外部记忆，Qwen3-8B 在 ALFWorld 到 96.9%，长任务不用堆工具。
Meta 新研究提出 EvoHarness-RL，让智能体离线学习 harness 策略并在运行时在线构建外部状态。该策略利用 Belief、Progress、Experience 三类状态，先通过监督微调学习动作空间，再使用成本感知 GRPO 优化。基于 Qwen3-8B 的智能体在 ALFWorld 基准上达到 96.9%。训练中出现 harness annealing 和 harness evolution 两种动态，前者将重复模式吸收进模型策略，后者压缩工作区为紧凑状态。研究认为长程智能体更取决于可训练协调策略，而非更大工具或记忆。
New research from Meta. Agent harnesses are still mostly authored by hand. This makes it hard to t...
New research from Meta. Agent harnesses are still mostly authored by hand. This makes it hard to tune robust agent harnesses for long-horizon tasks. In this new work, agents learn harness policies offline and deploy them to construct and update external harness state online during runtime task execution. EvoHarness-RL learns that policy instead. Belief, Progress, and Experience are exposed as harness state the policy can act on. Supervised harness fine-tuning teaches the action space, then cost-aware GRPO explores when to read, update, and consolidate during a long run. Qwen3-8B reaches 96.9% on ALFWorld. Two dynamics come out of the training. > Harness annealing means recurring harness-use patterns get absorbed into the model policy, and the agent shifts from frequent calls toward selective access. > Harness evolution means progress updates and experience consolidation compress the workspace into a compact task-adaptive state. This shows that long-horizon agents get more from a trainable coordination policy than from bigger tools or larger memories. Paper: arxiv.org/abs/2608.05446 Track more trending AI papers in our academy: academy.dair.ai 💬 12 🔄 18 ❤️ 131 👀 10673 📊 46 ⚡